code: def actor_critic_loss(
      policy: Policy,
      model: ModelV2,
      dist_class: Type[TorchDistributionWrapper],
      train_batch: SampleBatch,
  ) -> Union[TensorType, List[TensorType]]:
      """Constructs the loss for the Soft Actor Critic.
  
      Args:
          policy: The Policy to calculate the loss for.
          model (ModelV2): The Model to calculate the loss for.
          dist_class (Type[TorchDistributionWrapper]: The action distr. class.
          train_batch: The training data.
  
      Returns:
          Union[TensorType, List[TensorType]]: A single loss tensor or a list
              of loss tensors.
      """
      # Look up the target model (tower) using the model tower.
      target_model = policy.target_models[model]
  
      # Should be True only for debugging purposes (e.g. test cases)!
      deterministic = policy.config["_deterministic_loss"]
  
      model_out_t, _ = model(
          SampleBatch(
              obs=train_batch[SampleBatch.CUR_OBS], _is_training=True
          ),
          [],
          None,
      )
  
      model_out_tp1, _ = model(
          SampleBatch(
              obs=train_batch[SampleBatch.NEXT_OBS], _is_training=True
          ),
          [],
          None,
      )
  
      target_model_out_tp1, _ = target_model(
          SampleBatch(
              obs=train_batch[SampleBatch.NEXT_OBS], _is_training=True
          ),
          [],
          None,
      )
  
      alpha = torch.exp(model.log_alpha)
  
      # Discrete case.
      if model.discrete:
          # Get all action probs directly from pi and form their logp.
          action_dist_inputs_t, _ = model.get_action_model_outputs(
              model_out_t
          )
          log_pis_t = F.log_softmax(action_dist_inputs_t, dim=-1)
          policy_t = torch.exp(log_pis_t)
          action_dist_inputs_tp1, _ = model.get_action_model_outputs(
              model_out_tp1
          )
          log_pis_tp1 = F.log_softmax(action_dist_inputs_tp1, -1)
          policy_tp1 = torch.exp(log_pis_tp1)
          # Q-values.
          q_t, _ = model.get_q_values(model_out_t)
          # Target Q-values.
          q_tp1, _ = target_model.get_q_values(target_model_out_tp1)
          if policy.config["twin_q"]:
              twin_q_t, _ = model.get_twin_q_values(model_out_t)
              twin_q_tp1, _ = target_model.get_twin_q_values(
                  target_model_out_tp1
              )
              q_tp1 = torch.min(q_tp1, twin_q_tp1)
          q_tp1 -= alpha * log_pis_tp1
  
          # Actually selected Q-values (from the actions batch).
          one_hot = F.one_hot(
              train_batch[SampleBatch.ACTIONS].long(),
              num_classes=q_t.size()[-1],
          )
          q_t_selected = torch.sum(q_t * one_hot, dim=-1)
          if policy.config["twin_q"]:
              twin_q_t_selected = torch.sum(twin_q_t * one_hot, dim=-1)
          # Discrete case: "Best" means weighted by the policy (prob) outputs.
          q_tp1_best = torch.sum(torch.mul(policy_tp1, q_tp1), dim=-1)
          q_tp1_best_masked = (
              1.0 - train_batch[SampleBatch.TERMINATEDS].float()
          ) * q_tp1_best
      # Continuous actions case.
      else:
          # Sample single actions from distribution.
          action_dist_class = _get_dist_class(
              policy, policy.config, policy.action_space
          )
          action_dist_inputs_t, _ = model.get_action_model_outputs(
              model_out_t
          )
          action_dist_t = action_dist_class(action_dist_inputs_t, model)
          policy_t = (
              action_dist_t.sample()
              if not deterministic
              else action_dist_t.deterministic_sample()
          )
          log_pis_t = torch.unsqueeze(action_dist_t.logp(policy_t), -1)
          action_dist_inputs_tp1, _ = model.get_action_model_outputs(
              model_out_tp1
          )
          action_dist_tp1 = action_dist_class(
              action_dist_inputs_tp1, model
          )
          policy_tp1 = (
              action_dist_tp1.sample()
              if not deterministic
              else action_dist_tp1.deterministic_sample()
          )
          log_pis_tp1 = torch.unsqueeze(
              action_dist_tp1.logp(policy_tp1), -1
          )
  
          # Q-values for the actually selected actions.
          q_t, _ = model.get_q_values(
              model_out_t, train_batch[SampleBatch.ACTIONS]
          )
          if policy.config["twin_q"]:
              twin_q_t, _ = model.get_twin_q_values(
                  model_out_t, train_batch[SampleBatch.ACTIONS]
              )
  
          # Q-values for current policy in given current state.
          q_t_det_policy, _ = model.get_q_values(model_out_t, policy_t)
          if policy.config["twin_q"]:
              twin_q_t_det_policy, _ = model.get_twin_q_values(
                  model_out_t, policy_t
              )
              q_t_det_policy = torch.min(
                  q_t_det_policy, twin_q_t_det_policy
              )
  
          # Target q network evaluation.
          q_tp1, _ = target_model.get_q_values(
              target_model_out_tp1, policy_tp1
          )
          if policy.config["twin_q"]:
              twin_q_tp1, _ = target_model.get_twin_q_values(
                  target_model_out_tp1, policy_tp1
              )
              # Take min over both twin-NNs.
              q_tp1 = torch.min(q_tp1, twin_q_tp1)
  
          q_t_selected = torch.squeeze(q_t, dim=-1)
          if policy.config["twin_q"]:
              twin_q_t_selected = torch.squeeze(twin_q_t, dim=-1)
          q_tp1 -= alpha * log_pis_tp1
  
          q_tp1_best = torch.squeeze(input=q_tp1, dim=-1)
          q_tp1_best_masked = (
              1.0 - train_batch[SampleBatch.TERMINATEDS].float()
          ) * q_tp1_best
  
      # compute RHS of bellman equation
      q_t_selected_target = (
          train_batch[SampleBatch.REWARDS]
          + (policy.config["gamma"] ** policy.config["n_step"])
          * q_tp1_best_masked
      ).detach()
  
      # Compute the TD-error (potentially clipped).
      base_td_error = torch.abs(q_t_selected - q_t_selected_target)
      if policy.config["twin_q"]:
          twin_td_error = torch.abs(
              twin_q_t_selected - q_t_selected_target
          )
          td_error = 0.5 * (base_td_error + twin_td_error)
      else:
          td_error = base_td_error
  
      critic_loss = [
          torch.mean(
              train_batch[PRIO_WEIGHTS] * huber_loss(base_td_error)
          )
      ]
      if policy.config["twin_q"]:
          critic_loss.append(
              torch.mean(
                  train_batch[PRIO_WEIGHTS] * huber_loss(twin_td_error)
              )
          )
  
      # Alpha- and actor losses.
      # Note: In the papers, alpha is used directly, here we take the log.
      # Discrete case: Multiply the action probs as weights with the original
      # loss terms (no expectations needed).
      if model.discrete:
          weighted_log_alpha_loss = policy_t.detach() * (
              -model.log_alpha
              * (log_pis_t + model.target_entropy).detach()
          )
          # Sum up weighted terms and mean over all batch items.
          alpha_loss = torch.mean(
              torch.sum(weighted_log_alpha_loss, dim=-1)
          )
          # Actor loss.
          actor_loss = torch.mean(
              torch.sum(
                  torch.mul(
                      # NOTE: No stop_grad around policy output here
                      # (compare with q_t_det_policy for continuous case).
                      policy_t,
                      alpha.detach() * log_pis_t - q_t.detach(),
                  ),
                  dim=-1,
              )
          )
      else:
          alpha_loss = -torch.mean(
              model.log_alpha
              * (log_pis_t + model.target_entropy).detach()
          )
          # Note: Do not detach q_t_det_policy here b/c is depends partly
          # on the policy vars (policy sample pushed through Q-net).
          # However, we must make sure `actor_loss` is not used to update
          # the Q-net(s)' variables.
          actor_loss = torch.mean(
              alpha.detach() * log_pis_t - q_t_det_policy
          )
  
      # Store values for stats function in model (tower), such that for
      # multi-GPU, we do not override them during the parallel loss phase.
      model.tower_stats["q_t"] = q_t
      model.tower_stats["policy_t"] = policy_t
      model.tower_stats["log_pis_t"] = log_pis_t
      model.tower_stats["actor_loss"] = actor_loss
      model.tower_stats["critic_loss"] = critic_loss
      model.tower_stats["alpha_loss"] = alpha_loss
  
      # TD-error tensor in final stats
      # will be concatenated and retrieved for each individual batch item.
      model.tower_stats["td_error"] = td_error
  
      # Return all loss terms corresponding to our optimizers.
      return tuple([actor_loss] + critic_loss + [alpha_loss])
  
imports: import gymnasium
  from gymnasium.spaces import Box, Discrete
  import logging
  import tree
  from typing import Dict, List, Optional, Tuple, Type, Union
  import ray
  import ray.experimental.tf_utils
  from ray.rllib.algorithms.sac.sac_tf_policy import build_sac_model, postprocess_trajectory, validate_spaces
  from ray.rllib.algorithms.dqn.dqn_tf_policy import PRIO_WEIGHTS
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical, TorchDistributionWrapper, TorchDirichlet, TorchSquashedGaussian, TorchDiagGaussian, TorchBeta
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.policy_template import build_policy_class
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.spaces.simplex import Simplex
  from ray.rllib.policy.torch_mixins import TargetNetworkMixin
  from ray.rllib.utils.torch_utils import apply_grad_clipping, concat_multi_gpu_td_errors, huber_loss
  from ray.rllib.utils.typing import LocalOptimizer, ModelInputDict, TensorType, AlgorithmConfigDict
label_desc: Use image node_id 4 for calling a global function with example usage: actor_critic_loss(policy, model, dist_class, train_batch) and returns: tuple